info:eu-repo/semantics/article
Predicting and Characterizing Neurodegenerative Subtypes with Multimodal Neurocognitive Signatures of Social and Cognitive Processes
Fecha
2021-08Registro en:
Ibanez Barassi, Agustin Mariano; Fittipaldi, Sol; Trujillo, Catalina; Jaramillo, Tania; Torres, Alejandra; et al.; Predicting and Characterizing Neurodegenerative Subtypes with Multimodal Neurocognitive Signatures of Social and Cognitive Processes; IOS Press; Journal of Alzheimer's Disease; 83; 1; 8-2021; 227-248
1387-2877
CONICET Digital
CONICET
Autor
Ibanez Barassi, Agustin Mariano
Fittipaldi, Sol
Trujillo, Catalina
Jaramillo, Tania
Torres, Alejandra
Cardona, Juan F.
Rivera, Rodrigo
Slachevsky, Andrea
Garciá, Adolfo
Bertoux, Maxime
Baez, Sandra
Resumen
Background: Social cognition is critically compromised across neurodegenerative diseases, including the behavioral variant frontotemporal dementia (bvFTD), Alzheimer's disease (AD), and Parkinson's disease (PD). However, no previous study has used social cognition and other cognitive tasks to predict diagnoses of these conditions, let alone reporting the brain correlates of prediction outcomes. Objective: We performed a diagnostic classification analysis using social cognition, cognitive screening (CS), and executive function (EF) measures, and explored which anatomical and functional networks were associated with main predictors. Methods: Multiple group discriminant function analyses (MDAs) and ROC analyses of social cognition (facial emotional recognition, theory of mind), CS, and EF were implemented in 223 participants (bvFTD, AD, PD, controls). Gray matter volume and functional connectivity correlates of top discriminant scores were investigated. Results: Although all patient groups revealed deficits in social cognition, CS, and EF, our classification approach provided robust discriminatory characterizations. Regarding controls, probabilistic social cognition outcomes provided the best characterization for bvFTD (together with CS) and PD, but not AD (for which CS alone was the best predictor). Within patient groups, the best MDA probabilities scores yielded high classification rates for bvFTD versus PD (98.3%, social cognition), AD versus PD (98.6%, social cognition+CS), and bvFTD versus AD (71.7%, social cognition+CS). Top MDA scores were associated with specific patterns of atrophy and functional networks across neurodegenerative conditions. Conclusion: Standardized validated measures of social cognition, in combination with CS, can provide a dimensional classification with specific pathophysiological markers of neurodegeneration diagnoses.